Yiwei Lu

I am a first year Ph.D. student in the David R. Cheriton School of Computer Science at the University of Waterloo, where I am supervised by Prof. Yaoliang Yu and Prof. Sun Sun. I am also a student affiliate of the Vector Instituite.

Previously, I have completed my M.Sc. in Computer Science at the University of Manitoba, where I was advised by Prof. Yang Wang. I did my bachelors at the University of Electronic Science and Technology of China. I was also an exchange student at UC Santa Barbara and an intern at Huawei Chengdu Research Center.

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  New! Feb 2022:I am joining Huawei Noah's Ark Lab in Montreal as an Intern.
  New! Nov 2021: One paper accepted as an oral to NeurIPS 2021 workshop on self-supervised learning.
  Feb 2021:One paper accepted to IEEE TMM.
  January 2021: I am joining National Research Council of Canada as an Intern
  September 2020: I am serving as a reviewer in AAAI 2021
  August 2020: I am presenting 1 paper in ECCV 2020
  August 2020: I am joining University of Waterloo and Vector Instituite as a Ph.D. student


I'm interested in machine learning and its application on computer vision. I am particularly interested in self-supervised learning and adversarial machine learning.

profile photo f-mutual Information Contrastive Learning
Guojun Zhang*, Yiwei Lu*, Sun Sun, Hongyu Guo, Yaoliang Yu
NeurIPS 2021 workshop on self-supervised learning   (Contributed Talk)
paper/ poster/ talk

We propose a general and novel loss function on contrastive learning based on f-mutual information.

profile photo AdaCrowd: Unlabeled Scene Adaptation for Crowd Counting
Mahesh Kumar Krishna Reddy, Mrigank Rochan, Yiwei Lu, Yang Wang
IEEE Transactions on Multimedia (TMM), 2021  
arXiv / code

We propose a new problem called unlabeled scene adaptive crowd counting.

profile photo Few-shot Scene-adaptive Anomaly Detection
Yiwei Lu, Frank Yu, Mahesh Kumar Krishna Reddy, Yang Wang
ECCV, 2020   (Spotlight)
arXiv / code

We propose a more realistic problem setting for anomaly detection in surveillance videos and solve it using a meta-learning based algorithm.

profile photo Structure Learning with Similarity Preserving
Zhao Kang, Xiao Lu, Yiwei Lu, Chong Peng, Wenyu Chen, Zenglin Xu
Neural Networks, 2020

We propose a structure learning framework that retains the pairwise similarities between the data points.

profile photo Future Frame Prediction Using Convolutional VRNN for Anomaly Detection
Yiwei Lu, Mahesh Kumar Krishna Reddy, Seyed shahabeddin Nabavi, Yang Wang
AVSS, 2019
arXiv / code

We propose a novel sequential generative model based on variational autoencoder (VAE) for future frame prediction with convolutional LSTM (ConvLSTM).

profile photo Similarity Learning via Kernel Preserving Embedding
Zhao Kang, Yiwei Lu, Yuanzhang Su, Changsheng Li, Zenglin Xu
AAAI, 2019

We propose a novel similarity learning framework by minimizing the reconstruction error of kernel matrices, rather than the reconstruction error of original data adopted by existing work.

profile photo Semantic Segmentation in Compressed Videos
Yiwei Lu, Ang Li, Yang Wang
MMSP, 2019

We propose a ConvLSTM-based model to perform semantic segmentation on compressed videos directly. This significantly speed up the training and test speed.


Anomaly Detection in Surveillance Videos using Deep Learning - Yiwei Lu, M.Sc.thesis, Department of Computer Science, University of Manitoba, June 2020.

Credits to Jon Barron for the website design.